Author: Razwan Arshad
The Future of Pipeline Integrity Management: From Fragmented Data to Predictive Insights
In a nutshell:
One of the most common misconceptions surrounding Artificial Intelligence (AI) is that algorithms alone create value. In practice, most organizations face a more fundamental challenge: fragmented data. AI is only as powerful as the data behind it.
Before organizations can build a data-driven integrity management program capable of moving beyond reacting to what has already happened and toward anticipating future threats, they must first establish a trusted, connected view of asset history, condition, and risk.
This article draws on practical experience and industry research to explore how integrity management is evolving from reactive decision-making to predictive insight. It outlines the foundations required for a data-driven integrity program and examines how connected data, predictive analytics, and AI can help organizations respond to rising challenges with greater confidence and resilience.
Introduction
Pipeline operators are under growing pressure: the energy transition is accelerating, regulations are tightening, infrastructure is aging, and of course, there’s the so-called Silver Tsunami – a wave of experienced professionals are retiring, taking critical know-how with them. At the same time, many organizations are still relying on fragmented systems, disconnected data, and informal processes that require that same know-how to function effectively, presenting a significant organizational risk.
This imbalance is exposing a deeper challenge. While meeting compliance requirements may check the right boxes, it does not guarantee that integrity strategies are truly effective. To achieve this, operators need the capability to measure performance, monitor outcomes, and make informed decisions based on accurate, connected data.
This challenge is compounded, as the results of most decisions can only be measured in the medium to long term.
While the tools to achieve this already exist, developments in AI open up new possibilities. Integrated, data-driven integrity programs give operators the visibility and control to spot risks earlier and improve decision quality across the board. As a result of better insight and stronger systems, cost control, efficiency, and improved risk management will naturally follow.
Increasingly, the differentiator is no longer access to data itself, but the ability to convert that data into recommended actions. As integrity datasets continue to grow in volume and complexity, predictive analytics and AI offer the opportunity to move beyond understanding what happened in the past and toward anticipating what may happen next. Operators who embrace data-driven strategies are able to spot risks sooner, plan maintenance more effectively, and control costs with greater confidence.
Why the old model no longer works
The balance between threat and mitigation is shifting, and many organizations are struggling to keep up. Regulatory pressure is rising, as are the demands of energy transition, public expectations, and the risks associated with aging infrastructure.
But while external challenges grow, internal systems are under increasing strain. Tacit organizational knowledge is being lost as experienced staff retire, and critical data remains scattered across disconnected platforms and folder structures. This is often accelerated when asset ownership or operatorship changes hands.
Traditional integrity management systems were not designed for this level of complexity. Inspection reports, GIS data, and operational inputs often live in isolated silos, making it difficult to build a clear, connected view of asset condition and risk. Without that visibility and capability to integrate disparate data streams, integrity programs remain reactive rather than predictive, facing the same recurrent issues.
This lack of visibility creates a reactive culture. Teams spend more time searching for answers than using data to prevent problems. It becomes difficult to measure whether mitigations are working or whether performance is improving over time. When information doesn’t flow, risks are missed, the same failures recur, and asset performance either flatlines or decreases.
Even when a system meets regulatory requirements, it may still fall short of delivering consistent outcomes. Without integration, it becomes difficult to measure whether mitigations are working, learn from past incidents, or benchmark performance and identify trends.
In many cases, these weaknesses remain hidden until a significant event exposes them. The consequences may range from financial inefficiencies and misallocated investment to much more serious operational, safety, or reputational impacts.
Signs your integrity management program may be holding you back
If these warning signs sound familiar, it may be time to reassess your approach to data and integrity management.
- Do you spend more time gathering data than using it to make decisions?
- Are you constantly reaching out to the same person for insights?
- Is it difficult to compare your performance or costs against industry benchmarks?
- Have you experienced repeated issues or failures that could have been prevented with better insight?
- Are maintenance costs rising or dominated by emergency interventions rather than planned work?
- Have lessons from past incidents failed to produce lasting change?
- Do you repeat mitigations that have already been performed?
From monitoring to modeling
Digital transformation in integrity management is not just about collecting and storing data. It is about using that data at scale to make better decisions and assess whether mitigation strategies are effective. With strong data governance and the application of AI, predictive models reveal risks earlier, improve response, and support smarter long-term planning.
One of the most significant developments in recent years has been the ability to analyze relationships across large and diverse datasets. Predictive analytics can identify degradation patterns, risk trends, and emerging threats that may not be visible when reviewing individual inspections or datasets in isolation. This represents a fundamental shift from periodic assessment toward continuous risk intelligence.
By combining data from across silos, such as inline inspection results, corrosion models, geospatial information, and structural analysis, operators gain a connected view of asset health. This makes it easier to direct resources to where they will have the most impact. With integrated data, decisions across the board improve because they are based on insight, not assumptions.
The result is greater confidence in both system performance and risk management, and over time, this leads to stronger safety records and more stable operations. Looking ahead, many organizations see the ultimate goal as a real-time digital twin of their assets. By combining sensor data, inspection results, operational information, and predictive analytics within a connected digital environment, operators can continuously assess risk, optimize maintenance activities, and make investment decisions based on the latest available insights. For the first time, advances in data integration, analytics, and AI are bringing this vision within reach.
A natural outcome of this more mature approach to integrity management is cost control, which helps organizations shift to a more predictable and financially sustainable OPEX approach, whilst capturing the “pipeline event history” often held informally as institutional memory in engineers.
From reactive spending to risk-based investment
Traditional budgeting often centers on reactive repairs, driven by unplanned failures and rising operational costs. While it may check the compliance box, it does little to improve long-term performance or reduce risk.
Predictive, data-informed models shift the focus from short-term fixes to targeted interventions that deliver measurable outcomes. By aligning investment with actual asset condition and risk, operators can move beyond compliance to achieve both performance and reliability.
Machine learning creates value when it converts data into decisions. The goal is not to replace experts, but to help them focus on the highest-risk issues, prioritize resources more effectively, and act sooner with greater confidence.
What “good” looks like in digital integrity management
Achieving real integration across datasets, systems, tools, processes and decisions requires more than rolling out software. Real progress only happens when people, workflows, and technology are aligned around shared goals. With the right structure in place, data becomes more usable, performance easier to measure, and teams better equipped to act.
This starts with leadership commitment to the transformation required to achieve this. The most effective programs are driven by executives who view integrity as a business priority, not just a compliance requirement. That means setting clear goals, providing optimized resources, and involving critical stakeholders from the beginning. The next step is laying the foundation: mapping workflows, defining roles, and building habits of continuous learning. Without executive sponsorship of transformation through to lived reality, desired outcomes are difficult to achieve.
With connected systems and data collated into a single source of truth, teams can act faster and identify opportunities before small issues grow. Lessons from the past inform future behavior and performance. Organizations that can combine historical integrity data with predictive analytics gain an additional advantage: the ability to test assumptions, benchmark performance, and prioritize interventions based on future risk rather than historical events alone. When monitored through regular reviews and benchmarking, progress becomes easier to track, and improvement more deliberate.
The future of integrity management is not defined by individual software solutions. It is built on connected data that enables analytics, decision support and automated workflows across the entire asset lifecycle. Organizations that treat data as a strategic asset will be best positioned to transform insight into action and action into measurable business value.
What good looks like in practice
- Digital integrity management is driven from the top as a part of digital transformation.
- Leadership actively sponsors and reviews the integrity management program.
- Data systems are centralized, accessible, and integrated across departments.
- Workflows, responsibilities, and reporting lines are clearly documented and regularly improved.
- Teams are trained, empowered, and accountable for using data in decision-making.
- KPIs and benchmarking are used to track performance, support investment, and foster learning.
When these elements are in place, digital integration becomes a catalyst for both operational excellence and business resilience.
Scaling integrity maturity
Industry experience has shown that a one-size-fits-all approach to integrity management transformation rarely succeeds. Organizations differ in maturity, data availability, operational context, and risk profile. Lasting change depends on aligning technology investments with organizational processes, culture, and operational realities.
A structured assessment of current integrity management practices helps identify the most pressing gaps and recommends practical, modular steps to build maturity incrementally, in an agile manner, and at a sustainable pace. Operators can strengthen their programs without overwhelming teams or disrupting daily operations.
Moving from quick fixes to a data-driven integrity program takes more than a new set of tools. It requires organizational change to capture existing good practices, engineering knowledge, and experience that often reside in silos. From there, organizations need a clear roadmap to establish integrity workflows, connect critical data streams, and build the digital platforms and system landscape that enable engineers and managers to effectively manage asset integrity.
Maturity goes beyond implementation, too. It requires teams to use the system effectively. Technology implementation alone rarely creates lasting value. Organizations achieve the greatest benefit when data-driven practices become embedded within daily decision-making processes and engineering workflows.
Scaling maturity is about gaining control over complexity. Most organizations start data-informed, relying heavily on reports and dashboards, then progress to data-aware, where data is blended with experience and context. At full maturity, a data-centric approach treats data as a core asset that drives strategy, builds resilience, and improves cost predictability.
Questions you should be asking today
For executive teams, the ability to ask sharp, relevant questions is essential for shaping strategy and driving improvement. Addressing these core issues at the board level ensures that integrity management remains a central business priority rather than a technical afterthought.
As you consider the future of your pipeline assets, these are the questions you should be able to answer:
Question 1
Question 2
Question 3
Question 4
Question 5
Question 6
Question 7
Question 8
Take the next step
Integrity management is entering a new phase. For many years, the industry's focus has been on collecting data and documenting compliance. The next challenge is transforming that information into meaningful foresight.
As predictive analytics, AI, and integrated data environments continue to evolve, organizations will increasingly have the ability to identify risk earlier, prioritize investment more effectively, and improve decision quality across their assets.
The question is no longer whether these technologies will shape the future of integrity engineering. The real question is how quickly organizations can build the data foundations, processes, and expertise needed to take advantage of them.
Razwan Arshad
Head of Consulting
With more than two decades of experience in the energy industry, Razwan Arshad leads consulting within ROSEN's Engineering & Consultancy business. His work focuses on helping operators move from fragmented processes and siloed knowledge toward connected, data-driven approaches to integrity management.
Throughout his career, he has observed that the biggest challenge is rarely a lack of data, but rather the ability to turn engineering knowledge and operational experience into decisions that can be applied consistently across an organization.
Our subject matter experts
Will Sharman
Principal Engineer
With 35 years of technical, operational, and management experience in the oil and gas industry, Will Sharman is a recognized subject matter expert in Pipeline Integrity Management Systems (PIMS) and pipeline repair.
Throughout his career, he has worked across both ends of the integrity management spectrum, starting with the repair and mitigation activities required to maintain asset integrity and later focusing on the management systems that help reduce, optimize, and prioritize those interventions.
Edmund Bennett
Head of R&D for Engineering and Consultancy
Edmund Bennett is the Head of R&D for Engineering & Consulting at ROSEN. He leads the development and delivery of advanced integrity management, analytics, and digital solutions for energy infrastructure operators worldwide.
He is a recognized specialist in machine learning and software-enabled engineering services. He focuses on transforming complex technical data into practical solutions that improve asset performance, safety, and decision-making.
David Niehüser
Head of Business Line Integrity Management Systems
David Niehüser is the Head of Business Line Integrity Management Systems at ROSEN. He is responsible for the strategy, development, and delivery of digital integrity management solutions.
He focuses on helping asset operators unlock value from data through advanced analytics, integrity management platforms, and connected digital services. This enables safer, more efficient, and data-driven decision-making across the asset lifecycle.